Method and apparatus for monitoring physiological parameter variability over time for one or more organs

ABSTRACT

A system is provided for leveraging the power of the analysis of variability over time, and which uses an underlying framework that can handle variability analyses across a distributed system in a consistent manner, in part by constructing a standard variability data file that includes several manifestations of the underlying data acquired using variability monitoring. The consistent and standard data files, along with the underlying framework enables a user to make use of a set of convenient display tools, while a central entity can provide connectivity to the distributed environment and provide a way to update the equipment and software to ensure consistent and relevant analyses. The system can be extended into many environments, including in-patient, out-patient and completely mobile/stand-alone users.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.12/752,902 filed on Apr. 1, 2010 which is a continuation ofInternational PCT Application No. PCT/CA2008/001720 filed on Oct. 1,2008 which claims priority from U.S. Provisional Patent Application No.60/977,179 filed on Oct. 3, 2007, all incorporated herein by reference.

TECHNICAL FIELD

The following relates generally to medical monitoring and has particularutility in monitoring of physiological parameter variability over timefor one or more organs.

BACKGROUND

Bacterial infection remains a major cause of suffering and death,particularly in patients with impaired host defence. Although there isextensive knowledge on the mechanisms, pathways, mediators,transcription factors, receptor levels and gene activation involved inthe host response to severe infection, which may lead to organdysfunction, the understanding of the whole system working in concerttypically has limitations.

In the clinical setting, current monitoring techniques have achieved ahigh level of sophistication, involving vital sign monitoring, labs, anda variety of radiology, microbiology and pathology tests. Although thesetests are generally adequate to reliably diagnose infection, thecriteria to diagnose infection are non-specific. Frequently, a gestaltof individually non-specific clinical signs and symptoms lead to thediagnosis of infection and initiation of antibiotic therapy. As such,the timing of diagnosis is imprecise, insensitive and subject tojudgement, which may lead to delay. In certain patient populations withincreased susceptibility or impaired reserve, the delay in diagnosis,even if measured in hours, may prove catastrophic. Clinicaldeterioration may be well underway prior to recognition and response.Late diagnosis of infection, rapid clinical deterioration, ICU admissionand organ dysfunction are not uncommon in the case histories ofcritically ill patients.

For example, severe sepsis and septic shock are the most common causesof mortality in critically ill patients, accounting for 10% of intensivecare unit admissions (Brun-Buisson C. The epidemiology of the systemicinflammatory response. Intensive Care Med. 2000; 26 Suppl 1:S64-74) and2.9% of all hospital admissions (Angus D C, Linde-Zwirble W T, LidickerJ, Clermont G, Carcillo J, Pinsky M R. Epidemiology of severe sepsis inthe United States: analysis of incidence, outcome, and associated costsof care. Crit. Care Med. 2001 July; 29(7):1303-10). Given the provenbenefit of early resuscitation in sepsis, there is additional imperativeto develop methods to diagnose infection earlier with potential to savelives.

In another example, neutropenia is an intended iatrogenic side effect ofmyeloablative chemotherapy, commonly employed in the management ofmalignant hematological diseases, most commonly leukemia and lymphoma.Consequently, the host's immune system is compromised leading toincreasing risk of opportunistic infections (Neth O W, Bajaj-Elliott M,Turner M W, Klein N J. Susceptibility to infection in patients withneutropenia: the role of the innate immune system. Br J. Haematol. 2005June; 129(6):713-22). Febrile illness during neutropenia is often thefirst indication of infection. It requires prompt antimicrobial therapywith possible hospitalization. Thus, depending on therapy, neutropenicpatients experience a variable risk of fever, but when fever occurs, itis synonymous with infection in the majority of patients.

Prognosis of neutropenic infection is largely dictated by the severityof the systemic inflammatory response syndrome (SIRS) and clinicalprogression to sepsis syndrome, severe septic shock and organ failure,with increasing risk of death. Overall, febrile neutropenic patientsadmitted to the intensive care unit with systemic inflammatory responsesyndrome display a mortality risk of 20%, increasing to 90% in thepresence of septic shock (Regazzoni C J, Khoury M, Irrazabal C, MyburgC, Galvalisi N R, O'Flaherty M, et al. Neutropenia and the developmentof the systemic inflammatory response syndrome. Intensive Care Med. 2003January; 29(1):135-8) Regression analysis demonstrated that mortalitywas not modified by age, malignancy or positive blood cultures,highlighting the importance of the host response in determining outcome.These results underscore the importance of early diagnosis and earlyidentification of severity of illness in the management of febrileneutropenic patients.

Complex systems are systems comprised of a dynamic web of a large andvariably interconnected number of elements. Arising from the complexinterconnection of the parts (e.g. bees, neurons) and their environment(i.e. non-equilibrium), a new entity called a complex system (e.g.beehive, CNS) arises possessing distinct systemic or emergent properties(e.g. capacity to make honey, cognition, memory). Given that systemicproperties are wholly distinct from the properties of the parts, complexsystems cannot be fully understood solely by understanding their parts,no matter how thorough that understanding may be (Gallagher R,Appenzeller T. Beyond Reductionism. Science. 1999; 284:79) Givenconvincing evidence as well as promising insights, it has been observedthat the host response to severe infection or injury, which may lead toorgan dysfunction, is indeed a complex non-linear system (Seely A J,Christou N V. Multiple organ dysfunction syndrome: exploring theparadigm of complex nonlinear systems. Crit. Care Med. 2000 July;28(7):2193-200).

Identifying the host response to severe insult as a complex system helpsexplain why unpredictable rapid deterioration in patients with infectionand unexpected clinical improvement with no identifiable cause, bothoccur frequently, as uncertainty and surprise are ubiquitous withincomplex systems. If critical illness is characterized by an altered andunpredictable complex systemic response, then there is an imperative tomonitor the whole system as a whole and do so over time, in order totrack the trajectory of the system. As temporal variability of the partsis produced from the integrity and complexity of the whole system, thenit has been hypothesized that continuous monitoring of variability offermeans to monitor the whole system over time (Seely and Christou).

The science of characterizing rhythms, referred to most commonly asvariability analysis, represents the means by which a time-series of abiologic signal is comprehensively characterized, utilizing an array oflinear and non-linear variability analysis techniques based uponnon-linear dynamics, chaos theory and mathematical physics (Seely A J,Macklem P T. Complex systems and the technology of variability analysis.Crit. Care. 2004 December; 8(6):R367-84). Each technique providesdifferent and complementary means to characterize patterns of variation.Within a complex systems paradigm, variability analysis offerstechnology to more directly monitor the underlying system producing thedynamics.

A variety of techniques exist to quantify and characterize variationover time, including Time Domain, Frequency Domain, Entropy, andScale-Invariant Analyses. Briefly, Time Domain analysis involves the rawdata measured over time, an analysis of overall variation (standarddeviation and range) and the degree to which data may be fit bystandardized distributions (e.g. normal, log-normal). Frequency Domainanalysis evaluates the frequency spectrum of a signal observed overtime. Any time series may be represented as a sum of regularoscillations with distinct frequencies, conversion from a time domain toa frequency domain analysis (and back) is made possible with amathematical transformation called the Fourier transform. WaveletAnalysis combines time and frequency domain variation information,providing a hybrid of time- and frequency-domain analysis. EntropyAnalysis provides a measure of the degree of information, irregularity,disorder or complexity within a biologic signal. Mathematicalcalculations produce single (e.g. approximate or sample entropy) ormultiple values (e.g. multiscale entropy) that reflect degree ofirregularity or complexity. Scale-invariant Analysis provides a measureof common patterns of variation present across all time scales.

This panel of variability analysis techniques was developed to helpcharacterize biologic signals. They have been applied to heart rate,respiratory rate, blood pressure, neutrophil count, temperature andmore; investigations have consistently demonstrated the following: (1)patterns of variability provide additional clinically useful informationregarding the absolute value of that parameter, (2) altered variation ispresent in association with age and illness, and (3) degree ofalteration correlates with severity of illness.

A reduction in heart rate variability (HRV) has long been utilized as ameans to identify fetal distress, as well as a marker of mortality riskin adult patients with heart disease. More recently, HRV evaluation hasbeen performed in the presence of infection, demonstrating reproduciblealteration in HRV in patients with sepsis, septic shock and organdysfunction. Of value to intensivists, the degree to which HRV isaltered in the presence of infection correlates with severity ofillness. The results of many recent studies strongly support thehypothesis that altered HRV provides an untapped means of earlyidentification of infection in adults.

In another environment, Multiple Organ Dysfunction Syndrome (MODS),defined by having two or more failing organ systems, is the clinicalsyndrome characteristic of the chronically, critically ill patients.MODS is the leading cause of mortality in intensive care unit (ICU)patients. MODS represents the sequential deterioration of organfunction, usually leading to death, occurring in patients who are on themost advanced ICU life support technology possible. These patientsrequire considerable human and hospital resources, including invasivemonitoring in an ICU, one-on-one nursing, multiple transfusions,ventilators, dialysis, cardiac assist devices, vasopressors and more.

Evaluation of variability of patient parameters has only recently comeunder investigation in medical science, and is generally not used inroutine clinical practice. As discussed above, variability describes thedegree and character to which a parameter fluctuates over time. It is aprincipal component of the dynamics of a variable, which refers to itspattern of change over time. A parameter may be relatively constant,demonstrating a low degree of variability, or wildly fluctuate with highvariability, or demonstrate decreased irregularity or complexity, ordecreased high frequency variability.

Generally, reduced variability and complexity are correlated withillness state, however, both increased and decreased variability ofindividual patient parameters are associated with disease states. Thepositive clinical significance of the evaluation of these individualvariables indicates that the evaluation of multiple patient parameterswill provide for clinically useful information.

U.S. Pat. No. 7,038,595 to Seely, published May 2, 2006, describes asystem for multiple patient parameter variability analysis and display.The system described in Seely, provides analysis and display of thevariability of multiple patient parameters monitored by bedside monitorsfor each patient over time. Each monitored patient parameter is measuredin real-time, data artefacts can be removed, and variability analysis isperformed based upon a selected period of observation. Variabilityanalysis of each interval of time yields variability of the patientparameters, which represents a degree to which the patient parameterschange over an interval time, to provide diagnostic informationparticularly useful in the detection, prevention, and treatment of MODSamong other uses.

Although such a system provides clinicians with variability data ofmultiple patient parameters simultaneously, along with the capabilityfor variability analysis over time, there as yet exists no completesolution for organizing use of the acquired data, in particular asidefrom configurations in the ICU environment, or for conveniently handlingdata from multiple acquisition sites.

SUMMARY

It has been recognized that the change in variability over time, whichcan correlate with illness state, can be more conveniently displayed byproviding additional variability display tools that enable a user tomanipulate generic displays of variability data acquired over aplurality of intervals, in a configurable display toolkit. It has alsobeen recognized that using a consistent variability data file for eachvariable (e.g. each organ), and combining the variability data fileswith corresponding waveform data files and other data pertaining to theuser or patient enables deployment of a distributed framework that canacquire variability data for a plurality of time intervals throughmultiple sites concurrently obtaining each data with a separatevariability analysis apparatus capable of monitoring one or morevariables (e.g. organs). It has also been recognized that such adistributed framework enables software and operational updates as wellas threshold information to be distributed to the multiple sites by acentral service thus providing a consistent and standardized approach toconducting variability analyses.

Given that altered variability has been demonstrated in patients withinfection, and correlation with severity of organ failure, the followingsystem for conducting variability analyses over time through adistributed framework, is designed to enable early diagnosis ofinfection and real-time prognosis of organ failure.

In one aspect, there is provided a method for supporting variabilityanalyses conducted over time at a plurality of sites, each variabilityanalysis comprising computing a measure of variability for a pluralityof time intervals for one or more parameters, each measure ofvariability indicative of a degree and character to which a respectiveparameter changes over an interval of time, the method comprising:providing a connection between a central service and the plurality ofsites; the central service obtaining from each of the plurality ofsites, a data package comprising one or more data files representingresults of one or more variability analyses conducted at a respectiveone of the plurality of sites; the central service storing the datapackages in a central database and making the database available forfurther processing; the central service providing threshold data to atleast one of the plurality of sites, the threshold data comprisinginformation pertaining to parameters of the variability analyses andbeing derived from the contents of the central database; and the centralservice providing update data to at least one of the plurality of sites,the update data comprising information for maintaining consistency amongthe operation of the plurality of sites.

In another aspect, there is provided a method for supporting variabilityanalyses conducted over time at a plurality of sites, each variabilityanalysis comprising computing a measure of variability for a pluralityof time intervals for one or more parameters, each measure ofvariability indicative of a degree and character to which a respectiveparameter changes over an interval of time, for each of the plurality ofsites, the method comprising: providing a connection between the siteand a central service; preparing a data package comprising one or moredata files representing results of one or more variability analysesconducted at the site; making the data package available to the centralservice to enable the central service to store the data package withother data packages in a central database and to make the databaseavailable for further processing; obtaining from the central service,threshold data comprising information pertaining to parameters of thevariability analyses and being derived from the contents of the centraldatabase; and obtaining from the central service, update data comprisinginformation for maintaining consistency of the site with others of theplurality of sites.

In yet another aspect, there is provided a method for supportingvariability analyses conducted over time at a plurality of sites, eachvariability analysis comprising computing a measure of variability for aplurality of time intervals for one or more parameters, each measure ofvariability indicative of a degree and character to which a respectiveparameter changes over an interval of time, the method comprising:providing a connection between a central service and the plurality ofsites; each of the plurality of sites preparing a data packagecomprising one or more data files representing results of one or morevariability analyses conducted at a respective site; the plurality ofsites making the data packages available to the central service; thecentral service obtaining from each of the plurality of sites, a datapackage comprising one or more data files; the central service storingthe data packages in a central database and making the databaseavailable for further processing; the central service providingthreshold data, the threshold data comprising information pertaining toparameters of the variability analyses and being derived from thecontents of the central database; the plurality of sites obtaining thethreshold data from the central service; the central service providingupdate data, the update data comprising information for maintainingconsistency among the operation of the plurality of sites; and theplurality of sites obtaining the update data from the central service.

In yet another aspect, there is provided a method for preparing a datapackage representing results of one or more variability analysesconducted at a respective site over time, each variability analysiscomprising computing a measure of variability for a plurality of timeintervals for one or more parameters, each measure of variabilityindicative of a degree and character to which a respective parameterchanges over an interval of time, the method comprising: obtaining awaveform for a parameter over a period of time comprising the pluralityof time intervals; using the waveform to obtain raw sensor datacomprising a raw time series; smoothing the raw sensor data to obtainsmooth sensor data; using the smooth sensor data to conduct avariability analysis to obtain raw variability data; smoothing the rawvariability data to obtain smooth variability data; associating timestamp data with the raw sensor data, the smooth sensor data, the rawvariability data, and the smooth variability data; generating avariability data file using the raw sensor data, the smooth sensor data,the raw variability data, the smooth variability data, and the timestamp data; and including the variability data file in the data package.

In yet another aspect, there is provided a method for performingvariability analyses conducted over time, each variability analysiscomprising computing a measure of variability for a plurality of timeintervals for one or more parameters, each measure of variabilityindicative of a degree and character to which a respective parameterchanges over an interval of time, the method comprising: obtainingclinical events recorded during the variability analysis; associatingone or more time stamps with the clinical events for correlating withdata obtained during the variability analysis; and associating theclinical events in a data package representing results of one or morevariability analyses for the one or more parameters.

In yet another aspect, there is provided a system for recording clinicalevents detected during variability analyses conducted over time, eachvariability analysis comprising computing a measure of variability for aplurality of time intervals for one or more parameters, each measure ofvariability indicative of a degree and character to which a respectiveparameter changes over an interval of time, the system comprising anevent recorder for capturing the clinical events, the event recordercomprising a display for providing an interface for a user, and acomputer readable medium comprising computer executable instructions forobtaining clinical events recorded during the variability analysis; andassociating one or more time stamps with the clinical events forcorrelating with data obtained during the variability analysis.

In yet another aspect, there is provided a system for displaying dataobtained during variability analyses conducted over time, eachvariability analysis comprising computing a measure of variability for aplurality of time intervals for one or more parameters, each measure ofvariability indicative of a degree and character to which a respectiveparameter changes over an interval of time, the system comprising adisplay toolkit and a data storage device for storing the data, thedisplay toolkit being embodied as a computer readable medium havingcomputer executable instructions for displaying time series dataextracted from sensor data along with variability data associated withthe time series data in the same screen.

It will be appreciated that these methods may be implemented as computerexecutable instructions on a computer readable medium and varioussystems may be configured to operate according to the methods asdescribed below.

BRIEF DESCRIPTION OF THE DRAWINGS

An embodiment of the invention will now be described by way of exampleonly with reference to the appended drawings wherein:

FIG. 1 is a schematic block diagram showing a centralized service forhandling data acquired from one or more variability analysis monitoringsites.

FIG. 2 is a schematic block diagram of the hospital site shown in FIG.1.

FIG. 3 is a schematic block diagram of the clinic site shown in FIG. 1.

FIG. 4 is a schematic block diagram of the mobile site shown in FIG. 1.

FIG. 5 is a schematic block diagram of the patient interfaces shown inFIGS. 2 to 4.

FIG. 6 is a schematic block diagram of the variability analysis serversshown in FIGS. 2 to 4.

FIG. 7 is a schematic block diagram of the variability and waveform datafiles shown in FIGS. 1 to 6.

FIG. 8 is a schematic block diagram of the display toolkit shown in FIG.5.

FIG. 9 is a block diagram illustrating exemplary displays for individualvariables.

FIG. 10A illustrates exemplary variability histograms.

FIG. 10B illustrates exemplary plots correlating variability histogramdata points for the variability histograms of FIG. 10A.

FIG. 10C illustrates exemplary review displays of variabilityhistograms.

FIG. 11 is a flow diagram illustrating the construction of a data fileas shown in FIG. 7.

FIG. 12 is a schematic block diagram of the central service shown inFIG. 1.

FIG. 13 is a diagram showing wavelet-based variability over time duringan exercise test.

FIG. 14 is a diagram illustrating the correlation between thewavelet-based variabilities shown in FIG. 13.

FIG. 15 shows a multi-parameter respiratory rate and heart ratevariability analyses.

FIG. 16 shows multi-parameter multi-organ respiratory rate and heartrate variability analyses corresponding to FIG. 15.

FIG. 17 shows smoothed heart rate variability for multiple patients.

FIG. 18 shows an output display for the Vcam tool shown in FIG. 8.

FIG. 19 shows an output display for the Vcorrector tool shown in FIG. 8.

FIG. 20 shows an output display for the Vcorder tool shown in FIG. 8.

FIG. 21 shows an output display for the Vmovie tool shown in FIG. 8.

FIG. 22 shows respiratory rate variability (RRV) results for aspontaneous breath trial.

FIG. 23 is an output display showing the combination of HRV and RRV timecurves to provide RRV vs. HRV trajectory curves.

FIG. 24 is an output display showing an example interface for enteringclinical events associated with a variability analysis.

FIG. 25 is an output display showing an example interface for selectinga variability analysis type.

FIG. 26 is a schematic block diagram showing a process forretrospectively analysing waveform data using the variability analysisserver.

DETAILED DESCRIPTION OF THE DRAWINGS

It has been recognized that the underlying theory behind the analysis ofvariability over multiple intervals of time (e.g. continuous variabilityanalysis) has a widespread application in many environments, e.g. fortreatment, early diagnosis and overall health monitoring.

It has also been recognized that the analysis of variability over timeallows for various clinical applications. One such clinical applicationis the evaluation of a patient's own variability, that is theindividualized change in variability that is detected by monitoringvariability over multiple intervals of time. As will be explained below,the evaluation of a patient's variability has many uses, e.g. indetecting the onset of disease, both in real-time and retrospectively.Another such clinical application is the evaluation of change invariability in response to an intervention. For example, this enablesthe system described below, and/or parts thereof, to assist cliniciansin the safety and timing of liberation from medical apparatus such asmechanical ventilation in critically ill patients.

In order to take advantage of the power of variability analysis overtime for the above reasons and many more, an underlying framework hasbeen developed that can handle multiple variability analyses overmultiple intervals of time, across a distributed system in a consistentmanner. This is accomplished, in part, by constructing and storing astandard waveform data file as well as a separate variability data filefor each variable being analyzed, that includes a comprehensivecharacterization of the underlying data acquired using variabilitymonitoring. The consistent and standard data files, along with theunderlying framework enables a user to make use of a set of convenientvariability display tools, while a central entity can provideconnectivity to the distributed environment and provide a way to updatethe equipment and software to ensure consistent and relevant analyses.The system can be extended into many environments, including in-patient,out-patient and completely mobile/stand-alone.

Turning now to the figures, in particular FIG. 1, a central service 10for obtaining, handling and processing data packages 18 obtained fromone or more variability analysis monitoring sites 16 is shown, themonitoring sites 16 which obtain measurement data and perform ananalysis of the variability of one or more parameters over multipleintervals of time to generate the data packages 18. It will beappreciated that a “variability analysis over time” or a “variabilityanalysis” in general, will hereinafter refer to the computation of ameasure of variability for a plurality of time intervals for eachpatient parameter, variable, organ etc. Each measure of variability isindicative of a degree and character to which a respective patientparameter changes over an interval of time, and each variabilityanalysis enables changes in variability of the patient parameter to beobserved over a period of time. A variability analysis as hereindescribed can be performed on one or more patient parameters, i.e.single parameter and/or multi-parameter (e.g. single-organ ormulti-organ), and the multiple measures of variability can be obtainedaccording to any suitable pattern such as intermittent, continuous, etc.

The service 10 is part of a distributed data file management system 12,which also includes or makes use of a interconnection medium or network,in this example the Internet 14, and one or more variability analysismonitoring sites 16. In this example, three monitoring sites 16 a-16 care shown, each having a different role in a different environment.Shown in this example is a hospital monitoring site 16 a, a clinic site16 b and a mobile site 16 c, each of which are explained in greaterdetail below.

There may be any number of monitoring sites 16 of any type (i.e. 16 a, bor c) in any combination using any topology as required by the overallsystem 12. As such, the provision of three sites 16, one of each type,is shown for illustrative purposes only. Moreover, it will beappreciated that the network 14 can be any network, whether is be alocal area network (LAN), wide area network (WAN), etc. providingwireless or wired access/communication in any suitable configuration. Inthis example, the Internet 14 is a particularly suitable medium forproviding the connectivity between the central service 10, and themonitoring sites 16 such that many geographical locations can beaccommodated, however, any other medium or intermediary would suffice,including direct connections in, e.g. a closed system. Examples mightinclude a network of ICUs located anywhere in the world, or a network ofbone marrow transplant centers. Each network has an individualizedembodiment for performing single or multi-organ variability analyses,suited to its own needs.

As shown in FIG. 1, the Internet 14 provides a medium for transferringdata between the central service 10 and the monitoring sites 16. Datapackages 18 that are created at the monitoring sites 16 can be uploadedto the central service 10 by the monitoring sites 16 as shown, or mayalso be downloaded or ‘pulled’ from the monitoring sites 16 by thecentral service 10, e.g. using a periodic poll, transfer or batchprocess. In either case, the data packages 18 are of a suitable formatto be transferred over the intermediary network, e.g. one or more datapackets, email attachments, streaming data, etc., when using theInternet 14. The data packages 18 may also be text files, or acombination of several file types such as text, graphics, audio etc. Itwill be appreciated that the data packages 18 need not be embodied asdiscrete portions or packets during transmission but instead may be sentas continuous or semi-continuous data streams that are received andprocessed at the central service 10. Accordingly, it can be seen thatthe data packages 18 shown in FIG. 1 represent generally the flow ofdata from the monitoring sites 16 to the central service 10 and anynetwork or signal provides a computer readable medium for carrying thedata represented by the data packages 18. As will be explained furtherbelow, each data package 18 generally represents a particulartransmission of data comprising one or more sets of a variability datafile 103 and corresponding waveform data file 104, each set beingassociated with a particular parameter. It will be appreciated that thetem'“organ” is used herein for illustrative purposes only and mayrepresent any parameter, variable, feature or item for which theanalysis of variability over time can be measured.

Two other types of data transfers are also shown in FIG. 1, namely forthreshold data 20 and update or upgrade data 22. The threshold data 20contains information pertaining to the various thresholds that may beused by the monitoring sites 16 when conducting a variability analysisand to determine when alerts should be sounded. In variability analysis,a threshold represents the distinction between physiology and pathologythat are specific to distinct patient populations as well as fordistinct clinical applications. In other words, e.g., thresholds maydiffer for bone marrow transplant patients when compared to postoperative patients or those admitted with congestive heart failure. Aswill be explained in greater detail below, the threshold data 20 istypically based on an amalgamation of data that has been obtained frommultiple patients or users across the entire distributed system 12,which enables different thresholds to be identified for differentclinical environments and patient populations. As such, the thresholddata 20 offers a more complete look into the effects of variability andways to look at the results of a variability analysis that wouldotherwise not be available without the configuration and connectivityshown in FIG. 1.

In addition, there may be methods used by which the variability data isamalgamated, creating an overall determination of pathology versusphysiology.

The update data 22 contains upgrades, updates and any other usefulinformation that is needed to maintain consistency across the entiresystem 12. As such, the connectivity in FIG. 1 also enables a consistentand standardized way in which variability analysis can be performed, inaddition to the collaboration of data offered by the threshold data 20.It can thus be seen that the configuration and connectivity provided bythe system 12 shown in FIG. 1 enables the central service 10 to maintaincontrol over the quality and consistency of the variability analysesbeing performed at all the connected monitoring sites 16. Also, bygathering the data packages 18 from all monitoring sites 16, the centralservice 10 has access to a wider range of results for providing usefulinformation not only as feedback by way of the threshold data 20 andupdates 22, but also for research and/or learning as will be explainedbelow. It will be appreciated that the threshold data 20 and update data22 can possess similar characteristics as the data packages 18 and thussuch details need not be reiterated.

An example of a hospital monitoring site 16 a is shown in FIG. 2. Theelements shown in FIG. 2 are meant to illustrate several possiblecomponents that may interact with one another at the hospital site 16 a,however, any number (or all) of these elements can be used or not usedin specific hospital sites 16 a depending on the actual equipment and/orpersonnel present at the hospital site 16 a and the needs of thepatients 26 and personnel. In addition, the parameters being monitored(and the monitors themselves) may differ from network to network. Aswill be explained, at each monitoring site 16, including the hospitalsite 16 a shown in FIG. 2, is at least one variability analysis server24 for using acquired data to conduct variability analyses over time andgenerate data packages 18 that can be viewed at the site and provided tothe central service 10. However, as shown, each variability analysisserver 24 can interface with multiple patients 26 and, as such,typically only one variability analysis server 24 is required at eachmonitoring site 16. The variability analysis server 24 gathers dataacquired from one or more patients 26 through individual patientinterfaces 28, computes the measures of variability (i.e. conductsvariability analyses) for one or more patient parameters, and connectsto the central server 10 through the Internet 14 for facilitating thetransfer and/or receipt of data packages 18, threshold data 20 andupdate data 22. As shown, there can be different types of patients 26such as those in the ICU or in a regular hospital ward.

The patient interfaces 28 monitor physiological parameters of thepatient 26 using one or more sensors 30. The data or patient parameterscan include any variable that can be accurately measured in real time orintermittently. The data may be obtained from a continuous waveform (ata certain frequency level, e.g. 100 Hz for a CO2 capnograph or 500 Hzfor an EKG), or taken as absolute measurements at certain intervals,e.g. temperature measurements. The sensors 30 and patient interfaces 28may include, for example, an electrocardiogram (ECG), a CO₂ capnograph,a temperature sensor, a proportional assist ventilator, anoptoelectronic plethymography, a urometer, a pulmonary arterialcatheter, an arterial line, an O₂ saturation device and others. Toprovide more meaning to the data acquired through the sensors 30,clinical events are associated with the data, through an act ofrecording time stamped events 32, which are typically entered by a heathcare worker 34 in the hospital (bedside) environment. Clinical (timestamped) events can be physical activity, administration of medication,diagnoses, life support, washing, rolling over, blood aspiration etc.The clinical events are associated with a specific time, which is thenalso associated with the data that is acquired at the same specific timeusing the sensors 30. It will be appreciated that the clinical eventscan also be recorded in an automated fashion, e.g. by utilizingalgorithms which detect events electronically and process such events todesignate them as clinical events or noise. In this example, the patientinterface 28 is configured to gather the time stamped event data 32concurrently with the sensor data 30, further detail being providedbelow. It may be noted that additional non-time-stamped information(e.g. demographics) can also be recorded for each patient.

As can be seen in FIG. 2, the variability analysis server 24 not onlyconnects to the patient interfaces 28 and the Internet 14, but also toseveral other components/entities within the hospital site 16 a. Forexample, the server 24 can interface with a hospital monitoring system38 such as a nurse's station, as well as a central monitoring and alertsystem 36. The central monitoring and alert system 36 is capable ofmonitoring the variability analyses performed by the variabilityanalysis server 24 in order to detect critical or potentially criticalsituations evident from such variability analyses and provide an alertor alerts to a medical professional 42, who can also receive datadirectly from the variability analysis server 24. The variabilityanalysis server 24 can be embodied as a fixed unit or a moveable unitsuch as on a cart, in order to facilitate movement about the hospitalsite 16 a to serve multiple patients 26 in multiple locations.Similarly, the variability analysis server 24 can be a proprietaryapparatus or can be embodied as an add-on to existing beside orcentralized equipment to minimize space.

The variability analysis server 24 can also interact with a bedsidemonitor 40, which may be made available to or otherwise represent anurse or other personnel that monitors the patient 26 at the bedside.Similarly, the variability analysis server 24 can also interact withsensor displays 44, which are associated with other medical equipmentsuch as ECGs, blood pressure sensors, temperature sensors etc. As notedabove, the variability analysis server 24 can be a separate, stand-aloneunit but may also be integrated as a plug-in or additional module thatin this case could be used or integrated with existing bedsidemonitoring equipment, displays and sensors. FIG. 2 also shows othermonitors 46 which can include any other monitoring system or equipmentthat either can provide useful medical data or patient data or wouldbenefit from the data acquired by the variability analysis server 24.Patient data 48, e.g. provided by an electronic patient database (notshown) or manually entered can also interact with the variabilityanalysis server 24. As will be discussed below, the patient data 48 maybe appended to, or included with the data packages 18 to provide furthercontext for the data contained therein. This enables patient specificssuch as age, general health, sex etc. be linked to the acquired data toassist in organizing data into demographics. As also shown in FIG. 2,the variability analysis server 24 can provide data or otherwise usefulinformation for local scientists 50 that are interested in or involvedin the implications and effects of variability. It will be appreciatedthat patient privacy and other concerns can be addressed as required, byadding data security or other de-identification measures.

Turning now to FIG. 3, a clinic site 16 b is shown. An example of aclinic site 16 b is a bone marrow transplant clinic. Similar to thehospital site 16 a discussed above, the clinic site 16 b includes avariability analysis server 24, that obtains data from one or morepatient interfaces 28, and connects to the Internet 14 for facilitatingdata transfer (i.e. to send data packages 18 and to receive thresholddata 20 and update data 22). In the clinic site 16 b, the patients 26are referred to as outpatients as they are not admitted to a hospital.The sensors 30, clinical events recorded as time stamped events 32 andpatient data 48 is acquired and used in a manner similar to thatdiscussed above and thus further details need not be reiterated.Similarly, the variability analysis server 24 can provide data andinteract with medical professionals 42 at the clinic site 16 b, as wellas local scientists 50, if applicable. The clinic site 16 b may includeone or more variability analysis servers 24, and would typically includea monitoring centre 52 that monitors the analyses of the variousoutpatients 26 and provides alerts if necessary. The monitoring centre52 enables the clinic's variability analysis server 24 to be monitoredfrom a remote location and allows personnel to monitor several servers24 if several are present in the clinic. In this way, a centralmonitoring centre 52 can be used to service several clinic sites 16 b.

A mobile site 16 c is shown in FIG. 4. The mobile site 16 c enables thecapabilities of the variability analysis server 24 to be used outside ofthe hospital and clinical environments and, as such, in this embodiment,the mobile site 16 c serves any “user” or “subject”. For the sake ofconsistency, hereinafter the term “patient” will refer collectively toany user or subject. In this way, it may be appreciated that variabilityanalyses can be performed on any user, including athletes, firefighters,police officers, or any other person that can benefit from monitoringvariability of one or more physiological parameters. This can thereforeextend to providing real-time monitoring in extreme environments such asduring a fire, in a mine, during rescue missions etc. where variabilitycan indicate a potentially critical situation. In all cases, variabilitycan be monitored over time and analyzed on an individual basis for anypatient 26 such that the resultant data is specific to that individual.Using the wider system 12 allows the central service 10 to takeadvantage of the individual results for many patients 26 and ascertainfurther and more complete information. The mobile site 16 c generallyrepresents any site that includes a variability analysis server 24,which connects to the central service 10 and can communicate with one ormore patients 26, whether they are patients in the traditional sense oranother type of user.

In the example shown in FIG. 4, the user 26 generally includes a mobiledevice 54 and has a number of sensors 30 that are in communication witha variability analysis server 24. The mobile device 54 can also be usedto provide inputs, e.g. for the time stamped event data 32, as well asto provide a display to the user 26 for entering parameters or to viewdisplay data 60 acquired by the sensors 30 and/or processed by theserver 24. The connections between the mobile device 54 and the server24, as well as between the sensors 30 and patient interface 28 can bewired or wireless and the variability analysis server 24 can be a fixedunit at a base station or a portable unit such as on a cart at amonitoring centre. The mobile device 54 can be a personal digitalassistant (PDA), mobile telephone, laptop computer, personal computer orany other device that can provide an input device, a display and someform of connectivity for interacting with the variability analysisserver 24, preferably in a completely mobile manner.

As noted above, each monitoring site 16 includes a variability analysisserver 24. Details of various embodiments of existing variabilityanalysis apparatus and configurations can be found in U.S. Pat. No.7,038,595 to Seely, the contents of which are incorporated herein byreference. As will be explained below in connection with FIGS. 5 and 6,the apparatus shown in Seely can be modified to work within the system12 by adding functionality and features for gathering, displaying andtransferring data, using a consistent procedure and consistent formats.First, the following provides further detail and examples regarding theacquisition of data by the sensors 30 and the patient interface 28.

Data acquisition involves the sequential recording of consecutive datafor each of the patient parameters under investigation. Examplesinclude: continuously recording cardiovascular parameter data;continuously recording respiratory parameter data; and recording otherpatient parameters at specified time intervals (e.g. glucose levelsevery 30 minutes).

As noted above, the data acquired for the variability analyses can beacquired from a continuous waveform, from which a time series can besampled; or taken intermittently as absolute measurements.

Patient parameters may be grouped into organ systems to facilitatepatient-monitoring and intervention. Table 1 shows patient parametersgrouped by organ system and the parameters in italicized font representthose that are taken from a waveform.

TABLE 1 Patient Parameters Variability Parameters by Organ SystemCardiovascular Respiratory Renal Liver CNS Heart Rate Respiratory RateUrine Output Arterial EEG Blood Pressure O₂ Saturation [Creatinine] pH[Glucose] Cardiac Output Arterial pO₂ lactate HCO₃ CVP Arterial pCO₂[LDH] MVO₂ Impedance* SVR Compliance* Tidal Volume* Anti- Inflam-Inflam- User User Phagocytic matory matory Specified** Specified** PMN#'s [TNF-α]*** [IL-10]*** Monocyte # [IL-1]*** [IL-4]*** PMN [IL-6]***Apoptosis*** *Airway impedance and pulmonary compliance are measurablein mechanically ventilated patients by using a Proportional AssistVentilator **The User Specified areas indicate the capacity to enter andorganize any additional parameters ***Parameters where new technologywould aid in safe, readily repeatable measurement (for example, withvery small blood volumes, in a regular, automated fashion) [ ] Denotes“concentration of”

Patient parameters that may be used to evaluate the integrity of thecardiovascular system include any parameter that can be accuratelymeasured at regular intervals (either from absolute measurements or froma waveform) that reflects the function of the heart and blood vessels.There are numerous potential variables amenable to variability analysisover time within the cardiovascular system. This includes heart rate,the first patient parameter that has undergone extensive evaluation ofits variability. The interval between heartbeats may be measuredprecisely by an electrocardiogram, and is known as the R-R′ interval.Other parameters that are part of the cardiovascular system and aresubject to real-time accurate measurement include blood pressure,cardiac output, central venous pressure, systemic vascular resistance,and others. Blood pressure may be measured with standard arterialin-dwelling catheters or with an automated brachial arterysphygmomanometer. Cardiac output may be continuously measured withtransesophageal echocardiography or chest impedance measurement. Centralvenous pressure may be measured by a catheter placed within the proximalsuperior vena cava. Other devices may prove to be more reliable oraccurate. Important to the selection of monitoring devices will be thelack of artefacts, ease of rapid measurement, and patient safety.Nonetheless, any parameter subjected to continuous, accuratemeasurement, if only for brief periods, can provide data for variabilityanalysis and display over time.

Parameters representing the integrity of the respiratory system includethose indicating adequate oxygenation of the blood and tissue,appropriate ventilation, arterial pH, respiratory rate and respiratorymechanics. The more accurate the measurements of the parameters, themore useful variability analysis over time becomes.

A situation in which a patient is on a mechanical ventilator deservesspecial mention. Most current ventilators deliver the same pressure orvolume to the patient from breath-to-breath. This limits, but does notcompletely abrogate the normal variability that is a component of anormal respiratory function. For example, if a patient is on pressuresupport, despite having the same pressure present to support theirventilation, there is slight variation in the tidal volume from breathto breath. Similarly, pressures may change slightly on volume controlventilation. It may therefore be possible to extract information onrespiratory variability using such ventilators. However, otherventilators exist which provide dynamic alteration of both pressure andvolume, which improves the significance of the respiratory variability.Specifically, a proportional assist ventilator permits thebreath-to-breath alteration and measurement of multiple respiratoryparameters, including airway resistance, pulmonary compliance, tidalvolume, peak airway pressure. Therefore, one use for the proportionalassist ventilator is where useful data to evaluate respiratoryvariability is provided.

Numerous other parameters, as shown in Table 1 (above), may be measuredand the resulting data stored for a subsequent variability analysis. Itis important to note that the patient parameters described do not forman exclusive list of patient parameters that can be analyzed using thevariability analysis server 24. Rather, the variability analysis server24 can accommodate any number of patient parameters that are subject toreal-time, accurate measurement. Thus, when technology becomes availableto measure other patient parameters, related data may be input alongwith the variables described, in order to provide an even more completeanalysis of physiologic or pathologic variability.

In the variability analysis server 24, a variability time series iscreated for each patient physiological parameter. First, the user canset the interval and step for data monitoring over a period of time.That is, the variability analysis is performed on an interval and movesstepwise through the data in time. Collecting the data involvesretrieving or accepting measured data points acquired by patientinterfaces 28, for example, and storing the data points for subsequentanalysis. The data collecting step also includes monitoring a quantityof data collected. For example, initial analysis may begin afterapproximately 1000 data points (for example 15 minutes of heart ratemeasurement) have been collected. For each patient parameter v_(k), auser, typically an attending physician, may select the number of datapoints m_(k) to collect in order to perform the variability analysis.Recommended settings may be provided by the central service 10 as well.

The method computes the time period represented by the selected numberof data points. Thereafter, all subsequent calculations are based on theperiod of time required to collect the m_(k) data points. Data updatespreferably occur as frequently as possible, preferably occurring eachinterval. An interval is defined as the time required to perform thevariability analysis for an individual patient parameter. Following theiteration of the next steps, the variability is re-evaluated based ondata collected since the last analysis was performed (i.e. next step).For example, if an interval is approximately 1 minute, about 100 datapoints of heart rate data are collected in each interval. The collecteddata displaces the oldest 100 data points previously stored, permittinga new variability analysis to be performed based upon the latest m_(k)data points. This process enables dynamic evolution of the analysis. Inorder to correlate data to a particular time period, time stamps areassociated with the data, as discussed above.

Turning now to FIG. 5, the patient interface 28, modified to be usedwithin the system 12, is shown in greater detail. The sensors 30, whichgather data from the patients 26 (or user 26) feed or otherwise makeavailable the acquired data to a data collection module 80 in thepatient interface 28. The data collection module 80 can be embodied insoftware, hardware or both, and also receives the time stamped eventdata 32. Between the sensors 30 and the data collection module 80, itwill be appreciated that analog-to-digital (A/D) conversion is typicallyperformed to convert the analog sensor data to digital data forsubsequent processing. In this embodiment, the time stamped events 32are captured through a time stamped event recorder 82. The time stampedevent recorder 82 provides an interface for, e.g. the health care worker34 to record the clinical events, which associates a particular eventwith a particular time. This can then be associated with the dataacquired from the sensors 30 at that time. The recorder 82 preferablyprovides both an input mechanism and display 84, which can be separatecomponents or can both be provided through a single mechanism such as atouchscreen. FIG. 24 is an example interface 300 that illustrates oneway in which to obtain clinical events 32 for the time stamped eventrecorder 82. It can be seen that several selection boxes 302 can beprovided to enable clinical event data to be recorded before the data isuploaded to the variability analysis server 24. The ability to “upload”waveform data and clinical data simultaneously enables, among otherthings, the following: comparison of clinical data and variability data,provision of a “report” encompassing both clinical and variability data,and performance of standardized multi-center research trials wherevariability is compared to standard clinical criteria. It will beappreciated that the recorder 82 can also be configured to receive audioor video inputs and can also be configured to automatically observe andrecord events, such as those that are triggered by another machine oreven through automated visual processing.

Some or all of the data that is collected by the data collection module80 can be used with a display toolkit 71 to display the raw data for theuser/patient on a display 73. The data can also be stored locally in adata storage device 86, or can be transferred directly through a datatransfer module 88. The data transfer module 88 represents the softwareand/or hardware that is used to provide connectivity between the patientinterface 28 and the server 24 and thus typically includes a transmitterconfigured for either wired or wireless transmission. The data transfermodule 88 can also be used to perform steps of datacompression/decompression or file conversion as needed.

In general, as shown in U.S. Pat. No. 7,038,595 to Seely, the datacollected by patient interfaces is stored, and then such data is thenavailable to a process for performing an individual patient variabilityanalysis, the output of which can be displayed. In some embodiments, theapparatus can be centralized, e.g. at a nurse's station in an ICU. Theindividual patient interfaces communicate data to a central processorfor multiple patient data collection. The collected data is then storedand is available to be processed by a multiple patient variabilityanalysis routine, the output of which can be displayed. A user interfacecan be provided with the apparatus, to permit a user to format andcontrol the multiple patient variability display. This, e.g., enables anurse at a nurse's station to monitor multiple patients in a ward, suchas an ICU. In another embodiment, both individual and patient andmultiple patient configurations can be used. Turning now to FIG. 6,further detail concerning the variability analysis server 24, which isconfigured to operate within the system 12, is shown.

As can be seen in FIG. 6, the data collected by the patient interfaces28 is transmitted to the server 24 and input to a raw data builder 64.The raw data builder 64 extracts raw data from the waveform 62 producedby the respective sensor 30. The raw data builder 64 can extract thetime-series from the waveform that then undergoes variability analysis;e.g., the inter-beat interval time series is extracted from theelectrocardiogram, the inter-breath interval extracted from thecapnograph, and so on. The waveforms 62 are also preferably stored in adata file storage device 76 for later use in building the data packages18 and can be used by a display toolkit 72 to output the waveform 62 ona display 74. The server 24 also preferably includes a user interface 75for interacting therewith. For example, as shown in FIG. 25, an GUI 304can be provided using the interface 75 for configuring the variabilityanalysis type 306 and other parameters. It will be appreciated that theGUI 304 can be customized for specific trials or studies or can providea generic interface. The output from the raw data builder 64 is fed to avariability data file builder 70, which creates the data packages 18 andappends any other files or related data thereto. This output is also fedto the display toolkit 72, which can output the raw data on the display74, and is also fed to a data cleaner 66. The data cleaner 66 identifiesand removes artefacts and other noise from the raw data such that it issuitable for use by a variability analysis module 68.

It may be noted that there are many techniques that can be used toquantify artefacts at each interval in the data, e.g. a Pointcaré Plot.Also, different variability analysis techniques (e.g. wavelet, frequencydomain etc.) have different thresholds for how much artefact can behandled without compromising the variability analysis. For example, thedata cleaner 66 first determines how much artefact is present and thendetermines which technique(s) can handle that amount of artefact. Forexample, a particular set of data may have too much artefact forperforming a fast Fourier transform, but could be handled by a waveletanalysis. More discussion of these techniques is provided later.

The variability analysis module 68 performs the variability analysis andreceives and processes the update data 22 and any other inputs necessaryto perform the variability analysis. As can also be seen, the thresholddata 20 is obtained by the variability analysis server 24 and used asappropriate. The variability analysis module 68 may output variabilitydata (i.e. separate from the data packages 18) if desired, which can beused by the display toolkit 72 to output on the display 74. Thevariability data file builder 70 also receives the results of thevariability analysis as an input for building the variability portion(s)of the data packages 18, and receives additional patient information 48if applicable. Prior to transmitting the data packages 18 to the centralservice 10, a data conditioning stage 78 is used to filter, amplify,compress and otherwise prepare the data for transmission. It can be seenin FIG. 6 that at any stage, the output data is preferably stored in thedata storage device 76 such that it may be accessed, processed andviewed at a later time or during the variability analysis.

It may be noted that the variability analysis module 68 can beconfigured for and programmed to perform any type of variabilityanalysis. Similarly, the data cleaner 66 can be programmed to performany desirable data cleaning or conditioning. The following provides moredetail on how the data cleaning and variability analysis may beperformed.

The first step in variability analysis is typically to select datapoints. This can be done at the data cleaning stage 66 or upon executionof the variability analysis module 68. Real data measurement systemsoften acquire spurious signals that are not relevant to the requiredanalysis. As discussed above, these spurious data points are referred toas artefacts, and it is desirable to remove them in order to makeanalysis more meaningful. There are many acceptable methods for findingand removing artefacts from sequences of data collected from a widevariety of medical devices. A plurality of methods may be used. As alsonoted above, one technique is to use a Pointcaré plot. A Pointcaré plotrepresents differences between consecutive data points. The absolutevalue of a difference between a data point and the preceding data point(X_(i)−X_(i-1)) is plotted on the x-axis, and the absolute value of adifference between the same data point and the subsequent data point|X_(i)−X_(i-1)| is plotted on the y-axis. A visual evaluation may beused to eliminate artefact data.

A current data point, and the previous data points may be collected anddisplayed on the same graph, giving the appearance of a cloud. A usercan draw a gate around the data points using tools available through theuser interface 75, and a pointing device, for example, thus excludingwidely divergent, artefactual data points. The benefit of the Pointcaréplot is that there is a dynamic display of the data in evolution, andthere is the ability to dynamically alter the gate. In addition, if toohigh a percentage of data falls outside the gate, an alarm signal ispreferably activated.

Again, other methods may also be used to remove artefactual data. Anabsolute value of a parameter may be plotted in succession on a timescale evolution plot, permitting rapid inspection of the data, andremoval of artefacts. The original measurement, whether it is an R-R′interval for heart rate, a blood pressure tracing, etc., is available topermit the data cleaner 66, or a user to determine whether individualpoints should be discarded or added. Thus, storage of data is useful notonly for analyzing the data but also reviewing and analyzing previouslyrecorded data. Data artefacts can thus be removed by inspection of theoriginal data measurements.

Several methods may be used to select the data. Different methods may beapplied to different data sets, with distinct data collectiontechniques. Therefore a user can select the method by which dataartefacts are removed using tools available through the user interface75. Certain methods of selecting the data are ideal for specific typesof data measurement. For example, a Pointcaré Plot has been found to besuitable for heart rate analysis.

It may be noted that in some cases, some of the variability measures (tobe exemplified below) can be unreliable in the face of significantnon-stationarity. Therefore, it is beneficial to monitornon-stationarity in addition to variability in order to correct anydefects.

The second step in variability analysis is computing all variabilityparameters for each of the respective patient parameters. Thevariability represents a measure of a degree and character to which aparticular patient parameter fluctuates over time. There are manymethods for performing variability analysis. There is no consensuswithin the scientific literature that a single method of variabilityanalysis is superior for all patient parameters. Heart rate variability(HRV) has been the most extensively studied, and despite considerableresearch, no method for determining variability has proved consistentlybetter than others. In fact, numerous authors have demonstrated theclinical utility of evaluating HRV using different methods. Differentpatient parameters may require different methods for evaluatingvariability, due to differences such as altered statistical propertiesof the frequency distributions of the respective patient parameters.

In one embodiment, the variability analysis server 24 is adapted todisplay several options for variability analysis to the user on thedisplay 74, and to advise the user through user interface 75 and/ordisplay 74, respecting a suggested method for a particular patientparameter, based upon an algorithm for evaluating the data sets.

Currently, the simplest method for computing variability parametersinvolves the calculation of mean and standard deviation of the frequencydistribution of a selected data set. This information can be updatedover time (e.g. continuously) and displayed visually as a graph.Statistical interpretation of the frequency distribution is dependentupon whether the distribution is normal or lognormal. There arestandardized means of evaluating whether a distribution is accuratelyrepresented by a normal or log-normal curve, which include evaluation ofkurtosis and skew. By calculating the kurtosis and skew, the user may bedirected towards choosing an appropriate distribution. By evaluating thefrequency distribution, the mean and standard deviation would representthe variability parameters for the particular patient parameter underevaluation.

In addition to the mean and standard deviation of the frequencydistribution, numerous other methods for computing variabilityparameters exist. Methods for evaluating variability include spectraland non-spectral analysis, time-frequency analysis (wavelet analysis),calculation of Lyapunov exponents, approximate entropy, and others(Seely and Macklem, 2004—cited above). Preferably the user is presentedthrough the user interface 75 with a choice of several methods, andassisted in selecting a particular method. The results of thevariability analysis yield a variability parameter for each patientparameter under evaluation. The variability parameter may then bedisplayed, and updated over time. In each cycle, the updated variabilityis displayed.

As shown in FIG. 9, the variability analysis process preferably beginswith a real-time display 512, 532 of the respective patient parameters,heart rate 512 and blood pressure 532 in one example. A Pointcaré plot514, 534 is used, for example, to eliminate data artefacts byestablishing a gate 516, 536. A frequency distribution histogram 518,538 is calculated using the squared difference from the mean of thePointcaré plot. This method is suitable for data sets that demonstrate1/f noise. It is a tool for generating a frequency distribution ofdispersion from the mean, where all values are positive. The data isplotted in frequency bins, where each bin represents a proportionalamount of variation, as measured by the squared difference from themean. The bins are represented as a histogram, with the frequency on they-axis, and increasing variation on the x-axis. The bins on the left arenormally most full because they represent very common, small variations.The bins on the right, with increasing x-axis, represent large frequencyvariations, and are usually smaller. In every cycle, the histogram isupdated. The Log-log Plot 520, 540 is simply a linear representation ofthe frequency distribution histogram 518, 538 on a log-log plot offrequency vs. variation. The straight-line distribution of points ischaracteristic of 1/f noise. The best fit of a straight line through thedata points may be derived using standard linear regression analysis,and can also help inform the user respecting the appropriateness of thisparticular technique. The variability analysis module 68 calculates theslope of the line 522, 542 of the log-log plot and the x-intercept 524,544. These values can be displayed as pairs of dynamic variabilityparameter histograms 526, 546. The slope is represented by one histogram528, 548 and the intercept by another histogram 530, 550.

In general, the display of variability involves a way by which a user isable to access the variability of patient parameters computed by thevariability analysis method selected by the user. One way for displayingvariability parameters is dynamic variability histograms 526, 546 (FIG.9) which are represented as columns that increase or decrease in heightbased on changes in the variability of patient parameters over time.

“Normal” ranges for the variability of each patient parameter for eachpatient can be determined by analysis over time. Continued research willalso provide guidance in this area. Alarms can be set so that if avariability histogram is within the normal range, it is displayed in onecolor (green, for example). If the value of the histogram rises above orfalls below the normal range, it is displayed in a different color (red,for example). The histograms 526, 546 are updated at every cycle.

FIG. 10A illustrates exemplary variability histograms similar to thoseshown in FIG. 9. Examples are illustrated for heart rate 602, bloodpressure 604 and cardiac output 606. Another useful value that can bedisplayed is a standard deviation of the most recently selected periodof variability analysis. This can be super-imposed on the variabilityhistograms as an “I” bar 620, 622, 624, 626, 630, 632.

As described above, the clinical therapeutic potential of variabilityanalyses of multiple parameters over time is the ability to distinguishpathologic from physiologic systemic properties by monitoring patternsof alterations in the variability of multiple patient parameters. Thus adisplay can be tailored to best represent the current state of anyindividual patient with a view to evaluating the physiologic andpathologic properties of individual organ systems, by following thevariability of parameters intrinsic to that system.

It may be recognized that different organ systems are interrelated andmutually dependent. However, it is beneficial to distinguish betweenorgan systems, because therapeutic intervention is commonly directedtowards individual organs. Examples of organ systems include thecardiovascular system, respiratory system, the hematologic system,central nervous system, liver and metabolic system, kidney and wasteexcretion system.

Thus, flexibility in the display of variability of multiple parametersshould be provided. The user may select various display options toprofile an organ system or a combination of interdependent organsystems. In addition, the user may select any one of: an individualpatient display adapted to display the variability of all monitoredparameters for an individual patient; an individual patient organspecific display, which can display a selected organ system for anindividual patient; a multiple patient display, which can simultaneouslydisplay the variability of patient parameters for all patients in amonitored ICU; and a user specified variability display, which candisplay the variability of user selected patient parameters.

The ability to review changes in variability of patient parameters overtime increases the clinical utility of the variability analysesperformed using these techniques. FIG. 10B illustrates a VariabilityReview display 634, 636, which is a visual representation of threeselected variability parameters 602, 604, 606. One graph 634, representsslope values of the selected parameters 608, 612, 616. The other graph636, represents the intercept values of the selected parameters 610,614, 618. In the examples shown in FIG. 10B, for each graph, the heartrate values are plotted on the x-axes 646, 652; blood pressure valuesare plotted on the y-axes 648, 654; cardiac output values are plotted onthe z-axes (depth) 650, 656. Alternatively, the z-axis (depth) can berepresented by shades of color. The current variability values arepreferably represented by a large dot 638, 640 and the most recentcalculated variability values over a set period of time are representedby small dots 642, 644. This permits a visual representation of thedata, to enable the user to observe movement of the “cloud of data” overtime, as well as any correlation between the selected parameters.

Continued research and user observation helps define desirablephysiological patterns of variability. Specific movement of the cloud ofdata may be desirable and may be stimulated using therapeuticinterventions. Thus, a variability review display can be used tofacilitate positive intervention.

In addition to the patient and organ specific displays, a display ofvariability may also be organized into three principal modes:Instantaneous Display, Review Display or Combined Display.

The Instantaneous Display mode provides real-time display of currentvariability parameters, the process by which data selection has takenplace, and the graphs related to the particular method of variabilityanalysis used for an individual patient parameter. This mode may be usedin any of the four user-selected displays (Individual Patient Display,Individual Patient Organ Specific Display, Multiple Patient Display andUser Specified Variability Display).

The Review Display (FIG. 10C) permits the user to identify the patternsof alteration in variability parameters over a selected period of time,for selected individual or multiple patient parameters. The ReviewDisplay provides the user with a time-compressed, animated display ofthe variability of selected patient parameters during any selected timeperiod for which data exists. This display mode is similar to a video ofthe variability over time. This display permits the user to determinethe progression of the variability of patient parameters of anindividual patient. It also permits the user to determine a response toan intervention, a general progression of illness, or a need for furtherintervention. Averages of variability in patient parameters, calculatedfor specific time periods (for example, four hours prior to and fourhours following an intervention) can be included in a Review Display.

The Combined Display mode provides a combination of real-time display ofcurrent patient parameters, as well as a display of a previous(specified) period of time.

FIG. 10C shows three examples of review display. The first row of FIG.10C shows an example of combined display in which the variability of apatient parameter 24 hours ago (658) is displayed beside the variabilityof 1 hour ago (660), and the variability in real-time (662).

The second row of FIG. 10C illustrates a review display in which avariability progression is displayed for a patient parameter showing aprogression of variability from 48 hours (664), 24 hours (666) and 1hour (668).

The last row of FIG. 10C shows another review display in which thevariability of the patient parameter is displayed at X days (670), Yhours (672) and Z minutes (674).

As will be explained in greater detail below, the display toolkit 72enables the user to extend from the above general display features intoa more sophisticated and convenient user interface (UI). The extendeddisplay capabilities can be realized, in part, due to the organizationof the acquired variability and waveform data.

It may be noted that although the above examples illustrate thereal-time analysis of variability over time through a direct interfacebetween the patient interfaces 28 and the variability analysis server24, as shown in FIG. 26, various other configurations are possible.Turning now to FIG. 26, it can be seen that waveform data obtained froma patient or user 26 can be acquired and stored as waveform data files104 at any time and associated with clinical data 310 for that user 26.The clinical data 310 can represent any data pertaining to the user, thenature of their disease, demographics, clinical events, etc. Thewaveform data files 104 and clinical data 310 can then be used at someother time to perform a retrospective analysis. In this example, a CaseReport Form 312 user interface can be provided at a workstation 310,which enables the data to be uploaded to the variability analysis server24. It may be noted that the variability analysis server 24 can be localor remote and thus the acquisition site can represent any location orentity that is capable of receiving and/or storing and/or processing thedata to be uploaded. The variability analysis server 24 may then processthe data retrospectively according to the principles exemplified aboveand a report 314 generated pertaining to the variability analysis of thedata that was uploaded. It will be appreciated that, as shown in FIG.26, the reports 314 can also be sent to or downloaded by the centralservice 10 and stored in a central database 96 (see also FIG. 12).Similarly, the data packages 18 comprising the data files, detail ofwhich is provided below, can also be provided to the central service 10by the variability analysis server 24. It can therefore be seen that theacquisition of patient data, subsequent variability analysis andstorage, processing and reporting of results can be accomplished in anysuitable physical configuration and the stages shown can be temporallyspaced if appropriate.

As noted above, the variability analysis server 24 processes thewaveform data 62 sent from the patient interfaces 28 to ultimatelycreate variability data files 103 for of the data packages 18, which canbe sent to the central service 10. FIG. 7 illustrates a general datastructure for the data packages 18. As discussed above, each datapackage 18 may comprise a variability data file 103 and a correspondingwaveform data file 104 for each organ, parameter or variable. Thevariability data file 103 includes several sets of data for eachparameter, each associated with a common time scale. As shown in FIG. 7,the variability data file 103 includes a set of clinical events 106 thatare obtained from the time stamped event data 32, e.g. as entered usingthe timestamp event recorder 82. The variability data file 103 alsoincludes raw sensor data 108 extracted from the waveform 62, smoothsensor data 110 created from the raw sensor data 108, raw variabilitydata 112 generated during the variability analysis stage 68, and smoothvariability data 114 created from the raw variability data 112. Thesesmoothed versions (110 and 114) are created from a roving average of thedata in the raw versions (108 and 112) with a certain interval and step.It can be seen that all sets of data 106-114 are time-stamped withrespect to each other through time stamp data 116. The time stamp data116 is acquired along with the waveform and clinical events such thatany data acquired by the sensors 30 is associated with a time stamp. Inorder to enable a user to view waveforms and other display outputs formultiple organ variability at the same time, a common time scale isused. The common time scale can be applied using any known techniquesuch as curve fitting the data from separate parameters, and thenselecting data corresponding to a point in time and finding the value oneach curve.

The variability data file 103 is also associated with a correspondingwaveform 62 by having associated with or appended thereto, acomplementary or corresponding waveform file 104. The waveform file 104also includes time stamp data 116 that enables the waveform 104 to bematched/aligned with the corresponding sets of data 106-114. The datapackages 18 also includes a set of file information 118, which may be inthe form of a header, footer, flag(s), etc. In general, the fileinformation 118 is any information that pertains to the structure andproperties of the data packages 18. As noted above, other data,typically associated with the patient being monitored, can also beappended to the data packages 18. As such, the data packages 18optionally include a set of appended data 120 which may include thepatient data 48 that was originally input to or obtained by thevariability analysis server 24. In the example shown in FIG. 7, the fileinformation 118 and appended data 120 is shown as being included in thevariability data file 103 but it will be appreciated that such data 118,120 can also be included in the waveform file 104 or as its ownauxiliary or appended data file (not shown). It will also be appreciatedthat common file information 118 and appended data 120 can be associatedwith all variability data files 103 and waveform data files 104 in thedata package 18. As such, it can be seen that any suitable datastructure for organizing these data elements can be used and the oneshown in FIG. 7 is purely illustrative.

FIG. 11 shows a flow diagram illustrating how the data packages 18 canbe constructed. For each new data file routine 199 (i.e. for eachparameter), at 200, the waveform 62 is obtained from the sensors 30, at206, the waveform data file 104 is created and, if applicable, appendedto a new variability data file 103 to create a new data package 18.While the waveform 62 is being acquired, the clinical events arerecorded at 202, e.g. using the time stamped event recorder 82. As such,the clinical events data 106 is updated in the new variability data file103 as events are recorded. At 204 the raw time series is recorded toproduce the raw sensor data 108. The raw sensor data 108 is then used at208 to create a smooth time series (i.e. a smoothed version of the rawsensor data 108) and added to the new variability data file 103 as thesmooth sensor data 110. At 210, the variability analysis is performed onthe data. At 214, the raw variability data 112 generated from thevariability analysis at 210 is recorded and added to the new variabilitydata file 103. This is performed using the appropriate variabilityanalysis techniques yielding a plurality of variability time-stampedvalues. From this, the smooth variability data 114 is generated andstored in the new variability data file 103. It can be seen that at 218,all of the data sets created in the preceding stages are amalgamated,along with the appropriate time stamps to create the new variabilitydata file 103. At the same time, the waveform data file 104 appended ifapplicable.

At 219, if another variable or parameter is being monitored, another newdata file is generated for that parameter by repeating steps 200-218.Once all parameters have been analyzed, the data package 18 is generatedby amalgamating all variability data files 103 and correspondingwaveform data files 104. The patient data 220 and other file information118 (not shown) may be added to each variability data file 103 or as acommon set of identification data per data package 18.

Turning now to FIG. 8, the display toolkit 72 is shown. It will beappreciated that the toolkit 71 in the patient interface 28 shown inFIG. 5 may include the same or similar components. The tools containedin the toolkit 72, as exemplified in the figures and explained below,represent examples of generic displays. The tools are designed to bemaximally configurable and user friendly, namely so that one can changethe length of the overall time series and the interval being consideredfor variability, as well as the step by which the interval moves forwardin time. As such, the user can set the length of data and the intervaland step as well as the type of variability analysis.

The toolkit 72 includes a temporary data file storage 124 for storing orcaching data packages 18 that are to be displayed and analysed using thetools included in the toolkit 72. The toolkit 72 also includes a displayformat module 126 to enable the tools to handle the specific data formatshown in FIG. 7 and to handle any updates 22 that are specific to howthe data is processed for display purposes. The toolkit 72, in thisexample, includes a Vcam tool 128, which enables a user to magnify avariability data set in order to view the original raw data used tocalculate the variability. This enables the user to also view differentdata sets (e.g. 106-114) together in order to compare, e.g. rawvariability data with smooth variability data. Also included is aVcorder tool 130, which enables the user to scan data packages 18 overtime, e.g. by going forward and backwards with respect to time to showhow variability has changed over such time. The Yearn tool 128 andVcorder tool 130 may also be embedded in the same tool. The toolkit 72also includes a Vcorrector tool 132, which is a display tool used toamend, annotate and otherwise change the data stored in a particularwaveform data file 104 to improve the understanding and/or accuracythereof. Also included is a Vmovie tool 133, which enables a set of dataover time to be constructed and viewed in motion as a movie to provide afurther way in which to view continuously changing variability atdifferent intervals. As can also be seen, the toolkit 72 preferablyinteracts with an input interface to enable the user to interact withand use the tools included in the toolkit 72.

As noted above, the tools (and displays provided thereby) in the toolkit72 represent examples of generic displays. The Vmovie tool 133 forexample, represents a generic form of display, where any type ofvariability graph can be displayed along with the raw data above (seealso FIG. 21 explained later), along with the interval of data that isbeing used for variability analysis, such that as the movie plays, thevariability graph changes reflecting the interval noted above.

Turning now to FIG. 12, further detail of the central service 10 isshown. The central service 10 includes a data collection module 90 forobtaining the data packages 18 sent over or downloaded from the Internet14, which then stores the data packages 18 in a central database 96. Thecentral database 96 generally represents any and all data storageperformed by the central service 10 and should not be limited to anynumber of storage components, databases, formats etc. The centralservice 10 also preferably includes an administrative interface 92,either external as shown or internal, which enables administrativepersonnel to control operation of the central service 10. The datapackages 18 stored in the central database 96 can be used by astatistics engine 100 for conducting evaluations of data from acrossvarious demographics or to target specific symptoms, trends, outliers,etc. The statistics engine 100 and the central database 96 may alsointeract with external research programs 94 that are under the controlof researchers and that use the data stored in the central database 96to conduct variability analyses across multiple patients. It may benoted that each data package 18 is specific to an individual and, assuch, the central database 96 provides a tool for researchers to haveaccess to data from many individuals over time to conduct more thoroughand detailed analyses.

The central service 10 also includes a threshold engine 102 which is asoftware module or routine that uses input from the statistics engine100 and the data stored in the central database 96 to generate a set ofthresholds to enable the variability analysis servers 24 to conductconsistent analyses. The threshold engine 102 thus generates thethreshold data 20 that can be sent over the Internet 14 to the variousmonitoring sites 16. Similarly, an update engine 98 is included, whichis a software module or routine that takes input from the administrativeinterface 92 to generate system updates by way of update data 22. Theupdate engine 98 generates update data 22 and distributes such data 22over the Internet 14 to the various monitoring sites 16.

The update data 22 comprises any update to the software that performsvariability analysis in the system 12. As explained above, eachmonitoring site 16 includes an analysis server 24 for performing thevariability analyses. Given the connectivity provided by the system 12shown in FIG. 1, the central service 10 can maintain up-to-date softwarethroughout by preparing updates for each monitoring site 16 anddistributing update data 22 to each variability analysis server 24.Since variability techniques are ever evolving and becoming moresophisticated, as new techniques and tools are developed, the server 24can be updated remotely without requiring technicians to visit themonitoring sites 16 or to have the variability analysis servers 24brought in to a service centre. Furthermore, the central service 10 canensure that all monitoring sites are properly updated to maintain theeffectiveness of the data gathering by requiring feedback or periodicpolling etc. The central service 10 may also use the connectivityprovided by the system 12 to charge a subscription service fee or peruse/per update charge to create a stream of revenue. It can therefore beseen that the connectivity of the system 12 enables the update data 22to re-synchronize and standardize software processes and formatsthroughout.

It should be noted that the update data 22 should also include the bestinterval and step as well as recommended variability techniques to beused for each clinical application and patient population. Therefore,the distributed system 12 can be leveraged to provide consistentinformation to each monitoring site 16.

The threshold data 20 represents generally the best threshold at whichto issue an alert or to on a detected condition when performing avariability analysis. The threshold data 20 can be an evolving set ofdata that is based on a collaboration of the data acquired by gatheringthe data packages 18 and possibly through researcher, scientist andmedical professional input. The threshold data 20 enables the centralserver 20 to continuously refine and update the operating and alertthresholds across the entire system 12 and also for specific clinicenvironments and different patient populations as discussed above.

Turning back to FIG. 8, the user interfaces for the tools included inthe display toolkit 72 will now be explained in greater detail. Thevariability data generated using the variability analysis module 68 isconveniently amalgamated with the time series sensor data 108, 110 andthe clinical events in the variability data file 103, which hasassociated therewith, a waveform data file 104. The variability data andwaveform data can be output to the display 74 of the server 24 and thedisplay toolkit 72 provides advanced features for analysing variabilityover time, using any suitable and available techniques.

For example, a range of variability analysis techniques can be used toassess heart rate (HR) and respiratory rate (RR) separately to provideindividual measures of HR variability (HRV) and RR variability (RRV), aswell as simultaneously to provide an overall measure of cardiopulmonaryvariability (CPV). Such techniques that are used, typically assess HRV,RRV, and CPV in real-time. The main techniques that will be used are asfollows:

1) Time Domain: Standard deviation and coefficient of variationstatistics are computed to evaluate signal variability. Time domainmeasures also involve computation of probability distribution curves(frequency histograms) which will result in statistics like kurtosis andskewness for assessing variability.

2) Frequency Domain Techniques: The analysis of the spectral frequencycontent of HR and RR signals are undertaken by utilizing the fastFourier transform (FFT).

3) Time-frequency Domain: With the help of wavelet analysis signals canbe analyzed in both time and frequency domain simultaneously to overcomeissues such as non-stationarity and noise.

4) Complexity Domain: The amount of entropy or complexity or informationin the analyzed signals can be assessed using the sample entropy(SampEn) and multiscale entropy (MSE) measures.

5) Scale-invariant Fractal Domain: The inherent fractal nature of HRVand RRV signals can be investigated with techniques such as thedetrended fluctuation analysis (DFA), and power law analysis. Thesetechniques will not only help in assessing signal variability, they willalso help in distinguishing between physiologic and pathologic statesbased on the slope and intercept derived from the power law equation.

In Table 2, which follows, the variability outcomes of an exemplarystudy is summarized:

TABLE 2 Summary of Variability Outcomes Variability OutcomesDescription 1. Time Domain Standard Deviation Frequency Histograms(probability distributions) 2. Frequency Domain Fourier FrequencySpectrum Analysis 3. Time-frequency Domain Wavelet Analysis 4. ScaleInvariant (Fractal) Power Law Analysis Domain Detrended FluctuationAnalysis (DFA) 5. Complexity Domain Sample Entropy (SampEn) MultiscaleEntropy (MSE)

Referring now to FIGS. 13 and 14, the initial results of assessingwavelet-based HRV, RRV and CPV during a first stage in an exercise testin a healthy control patient is shown. In FIG. 13, the uppermost timeseries shows the RR in breaths per minute, the middle time series showsFIR in beats per minute, which are both measured simultaneously usingsensors 30 on the same patient. It can be seen that the data in eachtime series is associated with a time scale. These time scales can bealigned for displaying multiple organ variability together as discussedabove with respect to FIGS. 9 and 10 using standard curve fittingtechniques. The lowermost time series in FIG. 13 displays the individualwavelet-based variabilities for the RR and HR signals. The solid linedepicts RRV and the dotted line depicts HRV. It can be seen that thereexists a correlation of approximately 91% between the wavelet-based HRVand RRV in the lowermost plot, i.e. both variability curves tend to dropsimultaneously after the initiation of the stage 1 testing. FIG. 14studies the correlation more closely by linearly regressing the HRV andRRV signals at each time point thus characterizing CPV. It can be seenin FIG. 14 that by increasing the level of exercise, the CPV drops fromquadrant two (high HRV, high RRV) to quadrant four (low HRV, low RRV).The display 74 and display toolkit 72 enable the user to visualize plotssuch as those shown in FIGS. 13 and 14, which then provides theopportunity to explore and analyse the data in a more sophisticated andregimented manner.

FIG. 15 shows a multi-parameter RRV and HRV analysis during the samestage 1 exercise trial. In addition to the wavelet-area correlationshown in FIG. 13, FIG. 15 also provides a scale invariant power lawanalysis, standard deviation plot, and DFA. It can be seen that thereexists a good correlation between RRV and HRV for all techniques. Again,the data packages 18 and display toolkit 72 can be used to display plotssuch as those shown in FIG. 15, which result from conducting thevariability analyses. Such convenient display of data enables a user tobetter realize the correlations and significance of data from study tostudy and patient to patient. FIG. 16 illustrates the correlation foreach statistical method by linearly regressing the HRV and RRV signalsat each time point as shown in FIG. 14. It can be seen that, in general,the CPV tends to fall in a similar fashion for all statistics studied.FIG. 23 illustrates another correlation between HRV and RRV using astandard deviation trajectory curve 308, with a heart rate variabilitytime series 310 and a respiratory variability time series 312conveniently displayed alongside the trajectory curve 308. It may benoted that the simultaneous depiction of change in two organ variabilityover a time period provides an example of the visualization capabilitiesof the system 12, in particular for performing continuous multi-organvariability analyses.

As mentioned above, each variability analysis server 24 can acquire datafrom multiple patients in at a monitoring site 16. This enables a user(e.g. doctor) to view variability analyses conducted for multiplepatients on the same display, as shown in FIG. 17. It may be noted thatsimilar outputs can be available to users at or having access to thecentral service 10, given that the central service 10 has access to datapackages 18 (which include the variability files 103) for many patients.This enables the threshold data 20 to be determined and refined.

In FIG. 17, a smoothed (wavelet) HRV for Patient #1 is shown in the toptime series, for Patient #2 in the middle time series, and for Patient#3 in the bottom time series. Baseline variability is shown byhorizontal grey lines, which are the respective means of the smoothedHRV curves from day minus one to day two. A 10% drop in baselinevariability is depicted by the first set of dotted vertical lines(denoted A₁, A₂, A₃), and the 20% drop in variability is depicted by thesecond set of dotted vertical lines (denoted B₁, B₂, B₃). The initiationof antibiotics is depicted by solid vertical lines (denoted C₁, C₂, C₃).For Patient #1, a 10% drop in baseline variability occurredapproximately 16 hours before the initiation of antibiotics (A₁C₁˜16 h)and a 20% drop in baseline variability occurred approximately 5 hoursbefore the initiation of antibiotics (B₁C₁˜5 h). For Patient #2, a 10%drop in baseline variability occurred approximately 36 hours before theinitiation of antibiotics (A₂C₂˜36 h) and a 20% drop in baselinevariability occurred approximately 23 hours before the initiation ofantibiotics (B₂C₂˜23 h). For Patient #3, a 10% drop in baselinevariability occurred approximately 114 hours before the initiation ofantibiotics (A₃C₃˜114h) and a 20% drop in baseline variability occurredapproximately 52 hours before the initiation of antibiotics (B₃C₃˜52h).It can be seen that data for multiple patients can be compared andparameters such as % drop in variability identified directly from thevariability data (in this case from the smooth variability data 114).The annotations shown in FIG. 17 can be made on print outs of thedisplay output or saved directly to the variability data file 103 byproviding a suitable interface device such as a touch screen or tablet.Such annotations can then be appended to the variability data file 103to assist in later research or analysis.

FIG. 22 illustrates an example similar to FIG. 17 but for RRV. It can beseen that in a successful spontaneous breath trial (SBT), there was noperceived change in RRV. However, in a successful SBT but failingextubation, there was a perceived decrease in RRV. It can also be seenthat a failed SBT shows a decrease in RRV. Some of the clinicalsignificance of these findings are as follows: altered variabilityduring the SBT offers a measure of increased stress or work during aSBT, and those patients exhibiting a large change in variability duringa SBT are more likely to fail extubation. These findings can also behelpful in predicting those patients who successfully liberated frommechanical ventilation, and preventing failure of extubation and theassociated need for urgent re-intubation, which is in itself alife-threatening event. Moreover, isolated changes in cardiac orrespiratory variability during a SBT may predict the cause of why apatient may fail extubation, and lead to preventative strategies toavoid extubation failure.

With respect to results shown in FIG. 22, it may be noted thatexpeditious yet safe liberation from mechanical ventilation is ofcritical importance in the care of the critically ill. Prolongedmechanical ventilation is associated with increased in-hospital and5-year mortality, and elevated costs after cardiac surgery (RajakarunaC, Rogers C A, Angelini G D, Ascione R: Risk factors for and economicimplications of prolonged ventilation after cardiac surgery. Journal ofThoracic and Cardiovascular Surgery 2005, 130:1270-1277), anddevelopmental delays in pediatric patients (Campbell C, Sherlock R,Jacob P, Blayney M: Congenital Myotonic Dystrophy: Assisted VentilationDuration and Outcome. Pediatrics 2004, 113:811-816). Medical patients inthe Intensive Care Unit (ICU) who require re-intubation after extubationhave elevated hospital mortality rates, at least partially attributableto failed extubation (Epstein S K, Ciubotaru R L: Independent Effects ofEtiology of Failure and Time to Reintubation on Outcome for PatientsFailing Extubation. American Journal of Respiratory and Critical CareMedicine 1998, 158:489-493; Epstein S K, Ciubotaru R L, Wong J B: Effectof failed extubation on the outcome of mechanical ventilation. Chest1997, 112(186-192); Epstein S K, Nevins M L, Chung J: Effect ofUnplanned Extubation on Outcome of Mechanical Ventilation. AmericanJournal of Respiratory and Critical Care Medicine 2000, 161:1912-1916;and Esteban A, Alia I, Gordo F, Fernandez R, Solsona J F, I. V, S. M, M.A J, J. B, D. C. et al: Extubation outcome after spontaneous breathingtrials with T-tube or pressure support ventilation. American Journal ofRespiratory and Critical Care Medicine 1997, 156:459-465).

A number of weaning parameters have been identified and studied in orderto detect readiness of a patient to be both weaned and subsequentlyliberated from ventilatory support (MacIntyre N R: Evidence-BasedGuidelines for Weaning and Discontinuing Ventilatory Support: ACollective Task Force Facilitated by the American College of ChestPhysicians; the American Association for Respiratory Care; and theAmerican College of Critical Care Medicine. Chest 2000, 120:375-396).Nonetheless, the science of successful liberation from a ventilator,commonly referred to as “extubation”, still remains a daily challenge,both in terms of selection of patients for extubation (who?) andidentifying the appropriate time of extubation (when?).

In order to address this problem, the system 12 aims to harness hiddeninformation contained in the dynamics of physiologic parameters toimprove clinician's ability to predict extubation failure. Variabilityanalysis documents the degree and patterns of change of physiologicparameters over intervals-in-time, and complements standardpoint-in-time monitoring.

Analysis of variability has been performed in isolated centers ofmulti-disciplinary academic excellence using disparate methods ofacquiring physiologic data, differing methods to identify and removeartefact, and slightly different means to calculate variability.Currently no solution is available for clinicians interested inmonitoring variability. The system 12 described herein enables suchvariability monitoring as discussed herein throughout.

Continuous variability monitoring provides the capacity to measurechange in variability occurring as a response to an intervention orinsult. For example, the change in both HRV and RRV can be evaluated asa result of a standard ICU intervention performed to assess patients'readiness for extubation, namely a spontaneous breathing trial (SBT).HRV and RRV provide a continuous measure of cardiopulmonary reserve oradaptability, and therefore, it has been found that maintaining stablecardiopulmonary variability (CPV) throughout a SBT may predictsuccessful separation from the ventilator, and conversely, a reductionin CPV manifest during a SBT predicts extubation failure.

As discussed above, the sensors 30 generate waveforms 62 that are storedas waveform data files 104. The waveform data 104 is then processes togenerate time series, e.g. inter-breath or inter-beat time series for RRand HR respectively, which is the raw sensor data 108. These time seriesare then smoothed to create smooth sensor data 110. The smooth sensordata 110 can be analysed to produce the variability data 112, which canthen be smoothed to produce the smooth variability data 114. FIG. 18shows an example display generated by the Vcam tool 138, which shows asnapshot of the four types of data stored in the variability data file103, on the same display screen 150. This enables a user to view boththe raw and smooth data both before and after the variability analysisis conducted, along a common time scale. This is possible by using thetime stamp data 116 which is also stored in the variability data file103. In FIG. 18, the smooth time series are shown together at the bottomof the screen and the raw data at the top, however, it will beappreciated that the time series can be paired, i.e. raw-smooth foreach, certain ones suppressed to focus on only one, or rearranged asdesired by the particular user. The Vcam display 150 can be implementedin any suitable way, such as using standard display window with knownand familiar functionality, or using a proprietary display interfacewhere appropriate. Zoom and/or windowing features can also be used tofocus in on a particular region in the display. It may be noted that thedisplay interfaces, such as the displays 73 and 74 may be customdisplays or may utilize commercially available equipment.

Turning now to FIG. 19, an display screen 160 for utilizing theVcorrector 132 tool is shown. It will be appreciated that the Vcorrector132 can utilize the same screen 160 or interface as the other tools,such that any combination of two or more or all can be usedsimultaneously in the same computing environment (e.g. embedded in asingle tool) for portability or modularity, or may utilize separatedisplays as illustrated herein. The display screen 160, includes ascrolling tool 162, which includes left and right scroll buttons 164,166 and a scroll bar 168, commonly used in UIs. The display 160 enablesthe user to scroll through data, both waveform data 104 and variabilitydata 103 over time, in both directions. This enables the user to notonly look at a snapshot that is of interest, but to also look forpatterns over time, or other spurious events that could be linked tocertain clinical events, which, as discussed above, are stored andassociated with the data packages 18 for each patient. FIG. 19exemplifies a waveform 62 for a CO₂ sensor that shows end-tidal CO₂readings for detecting breaths. The waveform 62 is processed using abreath detection algorithm to detect each breath and thus create theinter-breath, raw sensor data 108.

The markings 160 at the top of the waveform shows where the algorithmhas detected breaths, and the user can scroll through the data to removespurious data or otherwise incorrectly detected breaths. In oneembodiment, a left-click can be used to add a breath marker, and aright-click used to delete a breath marker. The user can thus panthrough the waveform data 104 and determine if the breath detectionalgorithm is working properly. This can be done before the raw sensordata 108 is produced, or after to generate new, corrected raw sensordata 108 in response to detection of an erroneous or suspect result. TheVcorrector tool 132 is an optional step in the overall analysis and maynot be needed in certain studies. It will be appreciated that the sametools can be used to pan through the variability data stored in thevariability data file 103, primarily for conducting analyses such asthose depicted in FIGS. 13-17.

FIG. 20 shows a display screen 180 for the Vcorder tool 130. It can beseen that the Vcorder tool 130 preferably provides an output to the userwhich is similar to what is shown using the Vcam 128 (see FIG. 18) withthe additional scroll bar 162 that is used in the Vcorrector 132. Assuch, the tools 128-132 can be implanted as extensions or variations ofeach other in a combined tool if desired with special features availablefor each variation. It can be seen in FIG. 20 that the Vcorder tool 130enables the user to scroll through the data in time. In this way, forexample, a user can display a series of data using the Vcam 128 thendecide to look through the data over time. The Vcorder tool 130 can thenbe chosen which loads more data and provides the scrolling capabilitiesdiscussed above. The user may then have the option of consolidating aportion of data into a movie-like output by using the Vmovie tool 133.

A display screen 190 for the Vmovie tool 133 is shown in FIG. 21. It canbe seen that the Vmovie 133 provides a running time series 192 thatshows, in this example, raw heart rate data on top with an intervalmarker 194 showing the interval of data that is being used forvariability analysis. Below is a variability graph 196 that changesreflecting the interval that is shown above by the interval marker 194.

The screens 150, 160, 180 and 190 can optionally be provided in oneapplication and/or consolidated display screen (not shown), whichenables the user to quickly move between the different tools and haveboth the waveform data 104 and variability data 103 loaded and availableto them at the same time. It can be appreciated that the Vmovie 133 andVcam 128 tools are preferably provided as extensions to the Vcorder tool130 such that a user can zoom or pan through the data, select a regionand display the four plots as shown in FIG. 18 at any point in the timeseries or can generate movies of change in variability over time withina certain interval of time. This can be done to offer a more intuitivemaster tool that provides all the features in a single application forthe user's convenience. The functionality of the tools in the displaytoolkit 72 can be upgraded and refined by having regular update data 22sent to each server 24 at each monitoring site 16.

An example showing a typical data flow between the central service 10and the monitoring sites 16 will now be discussed, making reference tothe figures described above.

During operation, the central service 10 obtains data packages 18 fromone or more monitoring sites 16 and prepares and distributes update data22 and threshold data 20 when appropriate. The following exemplifiesdata flow from an ICU patient at the hospital site 16 a to the centralservice 10 but it will be appreciated that similar principles and stepsare taken by the other monitoring sites 16 as needed.

At the hospital site 16 a, the ICU patient 26 is outfitted with avariety of sensors, which, in this example, obtain HR and RR data. Thedata acquired by the sensors 30, i.e. the waveforms, is transmitted tothe patient interface 28. In this example, the patient 26 has its ownpatient interface 28, but it will be appreciated that shared patientinterfaces can also be used. The patient interface 28 is capable ofacquiring multiple organ data, which is collected by the data collectionmodule 80. In the ICU, the waveforms 62 can be displayed for thehealthcare worker on the display 73 using a local display toolkit 71.The healthcare worker uses the time stamped event recorder 82 to recordclinical events that can be associated with the data acquired by thesensors 30. The data collection module 80 gathers the waveforms 62 andthe time stamped event data 32 and stores the data if necessary in thedata storage device 86 for later transfer to the server 24, or uses thedata transfer module 88 to immediately send the data to the server 24.

As can be seen in FIG. 6, the waveforms 62 are stored in their nativeform in the data storage device 76 at the server 24 as well as being fedinto the raw data builder 64 to create the time series used by thevariability analysis module 68 for conducting variability analyses.Variability data files 103 are then built, e.g. as shown in FIG. 11, andthe data conditioning module 78 amalgamates the variability data files103 and corresponding waveform data files 104 into a combined datapackage 18 that is suitable to be transmitted to the central service 10.

However, as discussed above, the display toolkit 72 enables the user tocorrect the waveform data (e.g. breath or heart beat detection) and toview, annotate and analyse the outcome of the variability analysis inmany ways. This can be done before the data package 18 is sent to thecentral service 10 and it will be appreciated that copies of the datapackages 18 would typically be stored locally for later use. The datapackages 18, when released by the user, are uploaded or sent to thecentral service 10. The central service 10 then receives or obtains thedata packages 18 using the data collection module 90 and stores the datafiles in the central database 96. Once the data packages 18 are stored,they can be used, as discussed above for further research and refinementof the variability analysis techniques, thresholds and to developupgrades to the software at the server by creating new update data 20.In this way, the data acquired from this ICU patient 26 can be comparedto other patients that may be in other sites 16 in geographically spacedlocations etc.

The central service 10 can, at any time, either periodically or on aneed-to basis, prepare and distribute threshold data 20 and update data22 according to the discussion above. It will be appreciated that thedata 20, 22 can be pushed to the monitoring sites 16 or pulled downusing any suitable and known data transfer mechanism and should not belimited to any particular one. Similarly, the research programs 94 andstatistics engine 100 can be utilized “off-line” or can be regimented toconduct regular refinements or data mining sessions. The administrationinterface 92 can also be used periodically or on a need-to basis. Theupdate data 22 and threshold data 20 can be built manually,automatically using prepared algorithms or a combination of both. Theconnectivity provided by the system 12 also provides a framework forsending alerts between monitoring sites, e.g. by way of emails. This maybe useful where outpatients move from a hospital site 16 a to a clinicsite 16 b or mobile site 16 c and information should be shared with aregular practitioner.

The data flows above may be done in real time or at any interval thatsuits the particular application and environment. In this way, regularmonitoring can be done at the site and alerts created locally, which arethen added as appended data to data packages 18 for a particularpatient, which are then uploaded or transmitted in bulk exchanges. Thisenables the data packages 18 to be analysed locally and annotated whenappropriate rather then immediately sending data directly to the centralservice 10. However, if a particular environment does not have localmonitoring, e.g. certain mobile sites, the central service 10 can beused to either do the monitoring or redirect data to an appropriatemonitoring centre (similar to the arrangement in a clinic site 16 b).

It can therefore be seen that the underlying theory behind variabilityanalysis over time has a widespread application in many environments,e.g. for treatment, early diagnosis, real-time prognosis and overallhealth monitoring. In order to take advantage of the power ofvariability analysis, the underlying framework described above canhandle variability analyses across a distributed system in a consistentmanner, in part by constructing a standard variability data file thatincludes several manifestations of the underlying data acquired usingvariability monitoring over time. The consistent and standard datafiles, along with the underlying framework enables a user to make use ofa set of convenient display tools, while a central entity can provideconnectivity to the distributed environment and provide a way to updatethe equipment and software to ensure consistent and relevant analyses.The system can be extended into many environments, including in-patient,out-patient and completely mobile/stand-alone.

Although the invention has been described with reference to certainspecific embodiments, various modifications thereof will be apparent tothose skilled in the art without departing from the spirit and scope ofthe invention as outlined in the claims appended hereto.

The invention claimed is:
 1. A method for enabling variability analysesto be conducted at a plurality of sites, said method comprising:providing a connection between a central service and at least a firstsite of said plurality of sites; said central service obtaining fromsaid first site, a data package comprising variability data generated atsaid first site from at least one variability analysis conducted overtime based on sensor data acquired from at least one patient interface,said variability analysis comprising computing a measure of variabilityfor a plurality of time intervals for at least one parameter, eachmeasure of variability indicative of a degree and character to which arespective parameter changes over an interval of time; said centralservice storing said data package in a central database configured tostore a plurality of data packages, and making said database availablefor further processing; said central service providing threshold data tosaid first site, said threshold data having been generated using saidplurality of data packages in said central database and comprising aplurality of thresholds each representing at least one of a patientpopulation and a clinical application, said threshold data to be used bysaid first site for conducting variability analyses using a selected oneof the plurality of thresholds; and said central service providingupdate data to said first site, said update data comprising informationfor maintaining consistency among the operation of said plurality ofsites in conducting variability analyses.
 2. The method according toclaim 1 wherein each site is a hospital site, a clinic site, or a mobilesite.
 3. The method according to claim 1 wherein said central service isremote from said plurality of sites, said data packages are eitherpushed to said central service or pulled from said central service, andsaid threshold data and said update data are either pushed to saidplurality of sites or pulled from said central service.
 4. The methodaccording to claim 1 further comprising providing an administrativeinterface at said central service for interacting with said centraldatabase.
 5. The method according to claim 1 further comprising aresearch program interface for enabling researchers to interact withsaid database.
 6. The method according to claim 1, wherein saidthreshold data comprises information pertaining to thresholds to be usedby said plurality of sites when conducting said variability analyses,said thresholds configured to be used to trigger one or more alerts. 7.The method according to claim 1, wherein said update data comprisesupgrades to software running at said plurality of sites.
 8. Anon-transitory computer readable medium comprising computer executableinstructions for enabling variability analyses to be conducted at aplurality of sites, said computer readable medium comprisinginstructions for: providing a connection between a central service andat least a first site of said plurality of sites; said central serviceobtaining from said first site, a data package comprising variabilitydata generated at said first site from at least one variability analysisconducted over time based on sensor data acquired from at least onepatient interface, said variability analysis comprising computing ameasure of variability for a plurality of time intervals for at leastone parameter, each measure of variability indicative of a degree andcharacter to which a respective parameter changes over an interval oftime; said central service storing said data package in a centraldatabase configured to store a plurality of data packages, and makingsaid database available for further processing; said central serviceproviding threshold data to said first site, said threshold data havingbeen generated using said plurality of data packages in said centraldatabase and comprising a plurality of thresholds each representing atleast one of a patient population and a clinical application, saidthreshold data to be used by said first site for conducting variabilityanalyses using a selected one of the plurality of thresholds; and saidcentral service providing update data to said first site, said updatedata comprising information for maintaining consistency among theoperation of said plurality of sites in conducting variability analyses.9. A system comprising a processor and memory, said memory comprisingcomputer executable instructions for enabling variability analyses to beconducted at a plurality of sites, said computer executable instructionscomprising instructions for: providing a connection between a centralservice and at least a first site of said plurality of sites; saidcentral service obtaining from said first site, a data packagecomprising variability data generated at said first site from at leastone variability analysis conducted over time based on sensor dataacquired from at least one patient interface, said variability analysiscomprising computing a measure of variability for a plurality of timeintervals for at least one parameter, each measure of variabilityindicative of a degree and character to which a respective parameterchanges over an interval of time; said central service storing said datapackage in a central database configured to store a plurality of datapackages, and making said database available for further processing;said central service providing threshold data to said first site, saidthreshold data having been generated using said plurality of datapackages in said central database and comprising a plurality ofthresholds each representing at least one of a patient population and aclinical application, said threshold data to be used by said first sitefor conducting variability analyses using a selected one of theplurality of thresholds; and said central service providing update datato said first site, said update data comprising information formaintaining consistency among the operation of said plurality of sitesin conducting variability analyses.
 10. A method for conductingvariability analyses at a first site of a plurality of sites, said firstsite comprising a processor, said method comprising operating saidprocessor at said first site for: providing a connection between saidfirst site and a central service; said first site preparing a datapackage comprising variability data generated at said first site from atleast one variability analysis conducted over time based on sensor dataacquired from at least one patient interface, said variability analysiscomprising computing a measure of variability for a plurality of timeintervals for at least one parameter, each measure of variabilityindicative of a degree and character to which a respective parameterchanges over an interval of time; said first site making said datapackage available to said central service to enable said central serviceto store said data package with other data packages in a centraldatabase and to make said database available for further processing;said first site obtaining from said central service, threshold datahaving been generated using said plurality of data packages in saidcentral database and comprising a plurality of thresholds eachrepresenting at least one of a patient population and a clinicalapplication, said threshold data to be used by said first site forconducting variability analyses using a selected one of the plurality ofthresholds; and said first site obtaining from said central service,update data comprising information for maintaining consistency of saidsite with others of said plurality of sites in conducting variabilityanalyses.
 11. The method according to claim 10 wherein each site is ahospital site, a clinic site, or a mobile site.
 12. The method accordingto claim 11 wherein said hospital sites comprise at least onevariability analysis server connected to said at least one patientinterface, each patient interface connected to at least one sensor formeasuring a respective parameter of a patient; and one or moreadditional interfaces to existing systems within said hospital site. 13.The method according to claim 11 wherein said clinic sites comprise atleast one variability analysis server connected to said at least onepatient interface, each patient interface connected to at least onesensor for measuring a respective parameter of a patient; and amonitoring centre connected to said at least one variability analysisserver.
 14. The method according to claim 11 wherein said mobile sitescomprise at least one variability analysis server connected to said atleast one patient interface, each patient interface connected to atleast one sensor for measuring a respective parameter of a patient;wherein said analysis server and said patient interface are hosted by amobile device.
 15. The method according to claim 10 wherein said centralservice is remote from said plurality of sites, said data packages areeither pushed to said central service or pulled from said centralservice, and said threshold data and said update data are either pushedto said plurality of sites or pulled from said central service.
 16. Themethod according to claim 10 wherein said threshold data comprisesinformation pertaining to thresholds to be used by said plurality ofsites when conducting said variability analyses, said thresholdsconfigured to be used to trigger one or more alerts.
 17. The methodaccording to claim 10, wherein said update data comprises upgrades tosoftware running at said plurality of sites.
 18. The method according toclaim 10, wherein said data package is prepared by: obtaining a waveformfor a parameter over a period of time comprising said plurality of timeintervals; using said waveform to obtain raw sensor data comprising araw time series; smoothing said raw sensor data to obtain smooth sensordata; using said smooth sensor data to conduct a variability analysis toobtain raw variability data; smoothing said raw variability data toobtain smooth variability data; associating time stamp data with saidraw sensor data, said smooth sensor data, said raw variability data, andsaid smooth variability data; generating a variability data file usingsaid raw sensor data, said smooth sensor data, said raw variabilitydata, said smooth variability data, and said time stamp data; andincluding said variability data file in said data package.
 19. Anon-transitory computer readable medium comprising computer executableinstructions for conducting variability analyses at a first site of aplurality of sites, said computer readable medium comprisinginstructions for: providing a connection between said first site and acentral service; providing a connection between said first site and acentral service; preparing a data package comprising variability datagenerated at said first site from at least one variability analysisconducted over time based on sensor data acquired from at least onepatient interface, said variability analysis comprising computing ameasure of variability for a plurality of time intervals for at leastone parameter, each measure of variability indicative of a degree andcharacter to which a respective parameter changes over an interval oftime; making said data package available to said central service toenable said central service to store said data package with other datapackages in a central database and to make said database available forfurther processing; obtaining from said central service, threshold datahaving been generated using said plurality of data packages in saidcentral database and comprising a plurality of thresholds eachrepresenting at least one of a patient population and a clinicalapplication, said threshold data to be used by said first site forconducting variability analyses using a selected one of the plurality ofthresholds; and obtaining from said central service, update datacomprising information for maintaining consistency of said site withothers of said plurality of sites in conducting variability analyses.20. A system comprising a processor and memory, the memory comprisingcomputer executable instructions for conducting variability analyses ata first site of a plurality of sites, said computer executableinstructions comprising instructions for: providing a connection betweensaid first site and a central service; preparing a data packagecomprising variability data generated at said first site from at leastone variability analysis conducted over time based on sensor dataacquired from at least one patient interface, said variability analysiscomprising computing a measure of variability for a plurality of timeintervals for at least one parameter, each measure of variabilityindicative of a degree and character to which a respective parameterchanges over an interval of time; making said data package available tosaid central service to enable said central service to store said datapackage with other data packages in a central database and to make saiddatabase available for further processing; obtaining from said centralservice, threshold data having been generated using said plurality ofdata packages in said central database and comprising a plurality ofthresholds each representing at least one of a patient Population and aclinical application, said threshold data to be used by said first sitefor conducting variability analyses using a selected one of theplurality of thresholds; and obtaining from said central service, updatedata comprising information for maintaining consistency of said sitewith others of said plurality of sites in conducting variabilityanalyses.